Journal of Real-Time Image Processing

, Volume 10, Issue 4, pp 725–739 | Cite as

A reconfigurable embedded vision system for advanced driver assistance

  • Gorka VelezEmail author
  • Ainhoa Cortés
  • Marcos Nieto
  • Igone Vélez
  • Oihana Otaegui
Special Issue Paper


Computer vision technologies can contribute in many ways to the development of smart cities. In the case of vision applications for advanced driver assistance systems (ADAS), they can help to increase road traffic safety, which is a major concern nowadays. The design of an embedded vision system for driver assistance is not straightforward; several requirements must be addressed such as computational performance, cost, size, power consumption or time-to-market. This paper presents a novel reconfigurable embedded vision system that meets the requirements of ADAS applications. The developed PCB board contains a System on Chip composed of a programmable logic that supports parallel processing necessary for a fast pixel-level analysis, and a microprocessor suited for serial decision making. A lane departure warning system was implemented in the case study, obtaining a better computational performance than the rest of the works found in the literature. Moreover, thanks to the reconfiguration capability of the proposed system a more flexible and extensible solution is obtained.


ADAS System on Chip Hardware/software codesign  Embedded systems Smart cities 



This work has been partially supported by the program ETORGAI 2011–2013 of the Basque Government under project IEB11. This work has been possible thanks to the cooperation with Datik-Irizar Group for their support in the installation, integration and testing stages of the project.


  1. 1.
    Aggarwal, A.: Embedded vision system (EVS). In: 2008 IEEE/ASME International Conference on mechatronic and embedded systems and applications. MESA 2008, pp. 618–621 (2008)Google Scholar
  2. 2.
    An, X., Shang, E., Song, J., Li, J., He, H.: Real-time lane departure warning system based on a single FPGA. EURASIP J. Image Video Process. 1, 1–18 (2013)Google Scholar
  3. 3.
    Anders, J., Mefenza, M., Bobda, C., Yonga, F., Aklah, Z., Gunn, K.: A hardware/software prototyping system for driving assistance investigations. J. Real Time Image Process. (2013). doi: 10.1007/s11554-013-0351-4
  4. 4.
    Aurigi, A.: Making the digital city: the early shaping of urban Internet space. Ashgate Publishing, Farnborough (2005)Google Scholar
  5. 5.
    Batty, M., Axhausen, K., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., Ouzounis, G., Portugali, Y.: Smart cities of the future. Eur. Phys. J. Spec. Top. 214(1), 481–518 (2012)CrossRefGoogle Scholar
  6. 6.
    Buch, N., Velastin, S., Orwell, J.: A review of computer vision techniques for the analysis of urban traffic. IEEE Trans. Intell. Transp. Syst. 12(3), 920–939 (2011)CrossRefGoogle Scholar
  7. 7.
    Chakraborty, S., Lukasiewycz, M., Buckl, C., Fahmy, S., Chang, N., Park, S., Kim, Y., Leteinturier, P., Adlkofer, H.: Embedded systems and software challenges in electric vehicles. In: Proceedings of the Conference on design, automation and test in Europe, DATE ’12, pp. 424–429 (2012)Google Scholar
  8. 8.
    Chang, S.L., Chen, L.S., Chung, Y.C., Chen, S.W.: Automatic license plate recognition. IEEE Trans. Intell. Transp. Syst. 5(1), 42–53 (2004)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Darouich, M., Guyetant, S., Lavenier, D.: A reconfigurable disparity engine for stereovision in advanced driver assistance systems. In: Sirisuk, P., Morgan, F., El-Ghazawi, T., Amano, H. (eds.) Reconfigurable Computing: Architectures, Tools and Applications. Lecture Notes in Computer Science, vol. 5992, pp. 306–317. Springer, Berlin (2010)CrossRefGoogle Scholar
  10. 10.
    Dong, Y., Hu, Z., Uchimura, K., Murayama, N.: Driver inattention monitoring system for intelligent vehicles: A review. IEEE Trans. Intell. Transp. Syst. 12(2), 596–614 (2011)CrossRefGoogle Scholar
  11. 11.
    Geronimo, D., Lopez, A., Sappa, A., Graf, T.: Survey of pedestrian detection for advanced driver assistance systems. IEEE Trans. Pattern Anal. Mach. Intell. 32(7), 1239–1258 (2010)CrossRefGoogle Scholar
  12. 12.
    Harrison, C., Eckman, B., Hamilton, R., Hartswick, P., Kalagnanam, J., Paraszczak, J., Williams, P.: Foundations for smarter cities. IBM J. Res. Dev. 54(4), 1–16 (2010)CrossRefGoogle Scholar
  13. 13.
    Hiraiwa, J., Amano, H.: An FPGA implementation of reconfigurable real-time vision architecture. In: 27th International Conference on advanced information networking and applications Workshops (WAINA), pp. 150–155 (2013)Google Scholar
  14. 14.
    Hsiao, P.Y., Yeh, C.W.: A portable real-time lane departure warning system based on embedded calculating technique. In: IEEE 63rd Vehicular Technology Conference. VTC 2006-Spring 6:2982–2986 (2006)Google Scholar
  15. 15.
    Jeng, M.J., Guo, C.Y., Shiau, B.C., Chang, L.B., Hsiao, P.Y.: Lane detection system based on software and hardware codesign. In: 4th International Conference on autonomous robots and agents. ICARA 2009, pp. 319–323 (2009)Google Scholar
  16. 16.
    Lee, S., Son, H., Choi, J.C., Min, K.: High-performance hog feature extractor circuit for driver assistance system. In: 2013 IEEE International Conference on consumer electronics (ICCE), pp. 338–339 (2013)Google Scholar
  17. 17.
    Lin, H.Y., Chen, L.Q., Lin, Y.H., Yu, M.S.: Lane departure and front collision warning using a single camera. In: 2012 International Symposium on intelligent signal processing and communications systems (ISPACS), pp. 64–69 (2012)Google Scholar
  18. 18.
    Malinowski, A., Yu, H.: Comparison of embedded system design for industrial applications. IEEE Trans. Ind. Inform. 7(2), 244–254 (2011)CrossRefGoogle Scholar
  19. 19.
    Mielke, M., Schafer, A., Bruck, R.: Asic implementation of a gaussian pyramid for use in autonomous mobile robotics. In: 2011 IEEE 54th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 1–4 (2011)Google Scholar
  20. 20.
    Nieto, M., Cortés, A., Otaegui, O., Arróspide, J., Salgado, L.: Real-time lane tracking using rao-blackwellized particle filter. J. Real Time Image Process. (2012). doi: 10.1007/s11554-012-0315-0
  21. 21.
    Pedre, S., Krajník, T., Todorovich, E., Borensztejn, P.: Accelerating embedded image processing for real time: a case study. J. Real Time Image Process. (2013). doi: 10.1007/s11554-013-0353-2
  22. 22.
    Samarawickrama, M., Pasqual, A., Rodrigo, R.: FPGA-based compact and flexible architecture for real-time embedded vision systems. In: 2009 International Conference on industrial and information systems (ICIIS), pp. 337–342 (2009)Google Scholar
  23. 23.
    Sánchez-Oro, J., Fernández-López, D., Cabido, R., Montemayor, A.S., Pantrigo, J.J.: Urban traffic surveillance in smart cities using radar images. In: Ferrández Vicente, J.M., Álvarez Sánchez, J.R., Paz López, F., Toledo Moreo, F.J. (eds) Natural and artificial computation in engineering and medical applications, lecture notes in computer science, vol. 7931. Springer, Berlin, pp. 296–305 (2013)Google Scholar
  24. 24.
    Sanders, L.: Secure boot of Zynq-7000 All programmable SoC. Application note XAPP1175 (v1.0), Xilinx (2013)Google Scholar
  25. 25.
    Sandino, D., Matey, L.M., Vélez, G.: Design thinking methodology for the design of interactive real-time applications. In: Marcus, A. (ed.) Design, user experience, and usability. Design philosophy, methods, and tools, lecture notes in computer science, vol. 8012. Springer, Berlin, pp. 583–592 (2013)Google Scholar
  26. 26.
    Schneiderman, R.: Car makers see opportunities in infotainment, driver-assistance systems [special reports]. IEEE Signal Process. Mag. 30(1), 11–15 (2013)CrossRefGoogle Scholar
  27. 27.
    Shaout, A., Colella, D., Awad, S.: Advanced driver assistance systems - past, present and future. In: 2011 Seventh International Computer Engineering Conference (ICENCO), pp. 72–82 (2011)Google Scholar
  28. 28.
    Shreejith, S., Fahmy, S., Lukasiewycz, M.: Reconfigurable computing in next-generation automotive networks. IEEE Embed. Syst. Lett. 5(1), 12–15 (2013)Google Scholar
  29. 29.
    Souani, C., Faiedh, H., Besbes, K.: Efficient algorithm for automatic road sign recognition and its hardware implementation. J. Real Time Image Process. 9(1), 79–93 (2014). doi: 10.1007/s11554-013-0348-z
  30. 30.
    Stein, F.: The challenge of putting vision algorithms into a car. In: 2012 IEEE Computer Society Conference on computer vision and pattern recognition workshops (CVPRW), pp. 89–94 (2012)Google Scholar
  31. 31.
    Stein, G., Rushinek, E., Hayun, G., Shashua, A.: A computer vision system on a chip: a case study from the automotive domain. In: IEEE Computer Society Conference on computer vision and pattern recognition-workshops, 2005. CVPR workshops, pp. 130–130 (2005)Google Scholar
  32. 32.
    Teich, J.: Hardware/software codesign: the past, the present, and predicting the future. Proc. IEEE 100(Special Centennial Issue):1411–1430 (2012)CrossRefGoogle Scholar
  33. 33.
    Toral, S., Barrero, F., Vargas, M.: Development of an embedded vision based vehicle detection system using an ARM video processor. In: 11th International IEEE Conference on intelligent transportation systems, 2008. ITSC 2008, pp. 292–297 (2008)Google Scholar
  34. 34.
    Turturici, M., Saponara, S., Fanucci, L., Franchi, E.: Low-power DSP system for real-time correction of fish-eye cameras in automotive driver assistance applications. J. Real Time Image Process. (2013). doi: 10.1007/s11554-013-0330-9
  35. 35.
    Vicomtech-IK4: Viulib: Computer Vision SDK. URL (2013)
  36. 36.
    Wójcikowski, M., Zaglewski, R., Pankiewicz, B.: FPGA-based real-time implementation of detection algorithm for automatic traffic surveillance sensor network. J. Signal Process. Syst. 68(1), 1–18 (2012)CrossRefGoogle Scholar
  37. 37.
    Wu, B.F., Huang, H.Y., Chen, C.J., Chen, Y.H., Chang, C.W., Chen, Y.L.: A vision-based blind spot warning system for daytime and nighttime driver assistance. Comput. Electr. Eng. 39(3), 846–862 (2013)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Gorka Velez
    • 1
    Email author
  • Ainhoa Cortés
    • 2
  • Marcos Nieto
    • 1
  • Igone Vélez
    • 2
  • Oihana Otaegui
    • 1
  1. 1.Vicomtech-IK4Donostia-San SebastiánSpain
  2. 2.CEIT and Tecnun (University of Navarra)Donostia-San SebastiánSpain

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